{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"float:left; border:none\">\n",
    "   <tr style=\"border:none\">\n",
    "       <td style=\"border:none\">\n",
    "           <a href=\"https://bokeh.org/\">     \n",
    "           <img \n",
    "               src=\"assets/bokeh-transparent.png\" \n",
    "               style=\"width:50px\"\n",
    "           >\n",
    "           </a>    \n",
    "       </td>\n",
    "       <td style=\"border:none\">\n",
    "           <h1>Bokeh Tutorial</h1>\n",
    "       </td>\n",
    "   </tr>\n",
    "</table>\n",
    "\n",
    "<div style=\"float:right;\"><h2>03. Data Sources and Transformations</h2></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and Setup\n",
    "\n",
    "First, let's make the standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.plotting import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses Bokeh sample data. If you haven't downloaded it already, this can be downloaded by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using data directory: /Users/bryan/.bokeh/data\n",
      "Skipping 'CGM.csv' (checksum match)\n",
      "Skipping 'US_Counties.zip' (checksum match)\n",
      "Skipping 'us_cities.json' (checksum match)\n",
      "Skipping 'unemployment09.csv' (checksum match)\n",
      "Skipping 'AAPL.csv' (checksum match)\n",
      "Skipping 'FB.csv' (checksum match)\n",
      "Skipping 'GOOG.csv' (checksum match)\n",
      "Skipping 'IBM.csv' (checksum match)\n",
      "Skipping 'MSFT.csv' (checksum match)\n",
      "Skipping 'WPP2012_SA_DB03_POPULATION_QUINQUENNIAL.zip' (checksum match)\n",
      "Skipping 'gapminder_fertility.csv' (checksum match)\n",
      "Skipping 'gapminder_population.csv' (checksum match)\n",
      "Skipping 'gapminder_life_expectancy.csv' (checksum match)\n",
      "Skipping 'gapminder_regions.csv' (checksum match)\n",
      "Skipping 'world_cities.zip' (checksum match)\n",
      "Skipping 'airports.json' (checksum match)\n",
      "Skipping 'movies.db.zip' (checksum match)\n",
      "Skipping 'airports.csv' (checksum match)\n",
      "Skipping 'routes.csv' (checksum match)\n",
      "Skipping 'haarcascade_frontalface_default.xml' (checksum match)\n"
     ]
    }
   ],
   "source": [
    "import bokeh.sampledata\n",
    "bokeh.sampledata.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "We've seen how Bokeh can work well with Python lists, NumPy arrays, Pandas series, etc. At lower levels, these inputs are converted to a Bokeh `ColumnDataSource`. This data type is the central data source object used throughout Bokeh. Although Bokeh often creates them for us transparently, there are times when it is useful to create them explicitly.\n",
    "\n",
    "In later sections we will see features like hover tooltips, computed transforms, and CustomJS interactions that make use of the `ColumnDataSource`, so let's take a quick look now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating with Python Dicts\n",
    "\n",
    "The `ColumnDataSource` can be imported from `bokeh.models`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.models import ColumnDataSource"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ColumnDataSource` is a mapping of column names (strings) to sequences of values. Here is a simple example. The mapping is provided by passing a Python `dict` with string keys and simple Python lists as values. The values could also be NumPy arrays, or Pandas sequences.\n",
    "\n",
    "***NOTE: ALL the columns in a `ColumnDataSource` must always be the SAME length.***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = ColumnDataSource(data={\n",
    "    'x' : [1, 2, 3, 4, 5],\n",
    "    'y' : [3, 7, 8, 5, 1],\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up until now we have called functions like `p.circle` by passing in literal lists or arrays of data directly, when we do this, Bokeh creates a `ColumnDataSource` for us, automatically. But it is possible to specify a `ColumnDataSource` explicitly by passing it as the `source` argument to a glyph method. Whenever we do this, if we want a property (like `\"x\"` or `\"y\"` or `\"fill_color\"`) to have a sequence of values, we pass the ***name of the column*** that we would like to use for a property:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
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      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"37179421-f086-4c82-a17d-5077bcff2d38\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1012\"}],\"center\":[{\"id\":\"1015\"},{\"id\":\"1019\"}],\"left\":[{\"id\":\"1016\"}],\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"1037\"}],\"title\":{\"id\":\"1040\"},\"toolbar\":{\"id\":\"1027\"},\"x_range\":{\"id\":\"1004\"},\"x_scale\":{\"id\":\"1008\"},\"y_range\":{\"id\":\"1006\"},\"y_scale\":{\"id\":\"1010\"}},\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1036\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1017\",\"type\":\"BasicTicker\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":20},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1035\",\"type\":\"Circle\"},{\"attributes\":{\"axis\":{\"id\":\"1012\"},\"ticker\":null},\"id\":\"1015\",\"type\":\"Grid\"},{\"attributes\":{\"source\":{\"id\":\"1002\"}},\"id\":\"1038\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"data_source\":{\"id\":\"1002\"},\"glyph\":{\"id\":\"1035\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1036\"},\"selection_glyph\":null,\"view\":{\"id\":\"1038\"}},\"id\":\"1037\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1024\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1046\",\"type\":\"Selection\"},{\"attributes\":{\"formatter\":{\"id\":\"1044\"},\"ticker\":{\"id\":\"1017\"}},\"id\":\"1016\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1047\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1026\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1040\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1042\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1021\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1006\",\"type\":\"DataRange1d\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1020\"},{\"id\":\"1021\"},{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"},{\"id\":\"1025\"}]},\"id\":\"1027\",\"type\":\"Toolbar\"},{\"attributes\":{\"overlay\":{\"id\":\"1026\"}},\"id\":\"1022\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1020\",\"type\":\"PanTool\"},{\"attributes\":{\"formatter\":{\"id\":\"1042\"},\"ticker\":{\"id\":\"1013\"}},\"id\":\"1012\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1004\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"axis\":{\"id\":\"1016\"},\"dimension\":1,\"ticker\":null},\"id\":\"1019\",\"type\":\"Grid\"},{\"attributes\":{\"data\":{\"x\":[1,2,3,4,5],\"y\":[3,7,8,5,1]},\"selected\":{\"id\":\"1046\"},\"selection_policy\":{\"id\":\"1047\"}},\"id\":\"1002\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1013\",\"type\":\"BasicTicker\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"37179421-f086-4c82-a17d-5077bcff2d38\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"4655a1f3-6b9d-4bf5-9bd2-a22d7c0744bd\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=400, plot_height=400)\n",
    "p.circle('x', 'y', size=20, source=source)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: create a column data source with NumPy arrays as column values and plot it\n",
    "\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating with Pandas DataFrames\n",
    "\n",
    "It's also simple to create `ColumnDataSource` objects directly from Pandas data frames. To do this, just pass the data frame to  `ColumnDataSource` when you create it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.sampledata.iris import flowers as df\n",
    "\n",
    "source = ColumnDataSource(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use it as we did above by passing the column names to glyph methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"f905472c-131e-4ee4-bce5-eb3877027f61\" data-root-id=\"1103\"></div>\n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"d7ad2bde-3692-4110-879f-f1bdccddaf8f\",\"root_ids\":[\"1103\"],\"roots\":{\"1103\":\"f905472c-131e-4ee4-bce5-eb3877027f61\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1103"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=400, plot_height=400)\n",
    "p.circle('petal_length', 'petal_width', source=source)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: create a column data source with the autompg sample data frame and plot it\n",
    "\n",
    "from bokeh.sampledata.autompg import autompg_clean as df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatic Conversion\n",
    "\n",
    "If you do not need to share data sources, it may be convenient to pass dicts, Pandas `DataFrame` or `GroupBy` objects directly to glhyph methods, without explicitly creating a `ColumnDataSource`. In this case, a `ColumnDataSource` will be created automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"4e51ee65-b400-4a65-88b6-e43208bdc2fa\" data-root-id=\"1211\"></div>\n"
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Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"4d403011-cef6-4038-935c-5cd7a8583dc4\",\"root_ids\":[\"1211\"],\"roots\":{\"1211\":\"4e51ee65-b400-4a65-88b6-e43208bdc2fa\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1211"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.sampledata.iris import flowers as df\n",
    "\n",
    "p = figure(plot_width=400, plot_height=400)\n",
    "p.circle('petal_length', 'petal_width', source=df)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformations\n",
    "\n",
    "In addition to being configured with names of columns from data sources, glyph properties may also be configured with transform objects that represent transformations of columns. These live in the `bokeh.transform` module. It is important to note that when doing using these objects, the tranformations occur *in the browser, not in Python*. \n",
    "\n",
    "The first transform we look at is the `cumsum` transform, which can generate a new sequence of values from a data source column by cumulatively summing the values in the column. This can be usefull for pie or donut type charts as seen below."
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   "source": [
    "from math import pi\n",
    "import pandas as pd\n",
    "from bokeh.palettes import Category20c\n",
    "from bokeh.transform import cumsum\n",
    "\n",
    "x = { 'United States': 157, 'United Kingdom': 93, 'Japan': 89, 'China': 63,\n",
    "      'Germany': 44, 'India': 42, 'Italy': 40, 'Australia': 35, 'Brazil': 32,\n",
    "      'France': 31, 'Taiwan': 31, 'Spain': 29 }\n",
    "\n",
    "data = pd.Series(x).reset_index(name='value').rename(columns={'index':'country'})\n",
    "data['color'] = Category20c[len(x)]\n",
    "\n",
    "# represent each value as an angle = value / total * 2pi\n",
    "data['angle'] = data['value']/data['value'].sum() * 2*pi\n",
    "\n",
    "p = figure(plot_height=350, title=\"Pie Chart\", toolbar_location=None,\n",
    "           tools=\"hover\", tooltips=\"@country: @value\")\n",
    "\n",
    "p.wedge(x=0, y=1, radius=0.4, \n",
    "        \n",
    "        # use cumsum to cumulatively sum the values for start and end angles\n",
    "        start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),\n",
    "        line_color=\"white\", fill_color='color', legend_field='country', source=data)\n",
    "\n",
    "p.axis.axis_label=None\n",
    "p.axis.visible=False\n",
    "p.grid.grid_line_color = None\n",
    "\n",
    "show(p)"
   ]
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   "source": [
    "The next transform we look at is the `linear_cmap` transform, which can generate a new sequence of colors by applying a linear colormapping to a data source column. "
   ]
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\",\"dtype\":\"float64\",\"shape\":[4000]},\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]}},\"selected\":{\"id\":\"1521\"},\"selection_policy\":{\"id\":\"1522\"}},\"id\":\"1474\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"1442\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"16836ee3-1cc2-487e-bb5d-ff53f87760e2\",\"root_ids\":[\"1442\"],\"roots\":{\"1442\":\"e9452665-3e93-4274-91db-b224a68f29ba\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1442"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.transform import linear_cmap\n",
    "\n",
    "N = 4000\n",
    "data = dict(x=np.random.random(size=N) * 100,\n",
    "            y=np.random.random(size=N) * 100,\n",
    "            r=np.random.random(size=N) * 1.5)\n",
    "\n",
    "p = figure()\n",
    "\n",
    "p.circle('x', 'y', radius='r', source=data, fill_alpha=0.6,\n",
    "        \n",
    "         # color map based on the x-coordinate\n",
    "         color=linear_cmap('x', 'Viridis256', 0, 100))\n",
    "\n",
    "show(p) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the code above to use `log_cmap` and observe the results. Try changing `low` and `high` and specificying `low_color` and `high_color`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exercise: use the corresponding factor_cmap to color map a scatter plot of the iris data set\n",
    "\n",
    "from bokeh.sampledata.iris import flowers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Click on this link to go to the next notebook: [04 - Adding Annotations](04%20-%20Adding%20Annotations.ipynb).\n",
    "\n",
    "To go back to the overview, click [here](00%20-%20Introduction%20and%20Setup.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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